Added cool stuff

agent_with_all_agents_reflection.py

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+
+from swarms import Agent
+from swarm_models import OpenAIChat
+from swarms_memory import ChromaDB
+import os
+
+# Initialize memory for agents
+memory_risk = ChromaDB(metric="cosine", output_dir="risk_analysis_results")
+memory_sustainability = ChromaDB(metric="cosine", output_dir="sustainability_results")
+
+# Initialize model
+model = OpenAIChat(api_key=os.getenv("OPENAI_API_KEY"), model_name="gpt-4o-mini", temperature=0.1)
+
+# Initialize Risk Analysis Agent
+risk_analysis_agent = Agent(
+    agent_name="Delaware-C-Corp-Risk-Analysis-Agent",
+    system_prompt="You are a specialized risk analysis agent focused on assessing risks.",
+    agent_description="Performs risk analysis for Delaware C Corps.",
+    llm=model,
+    max_loops=3,
+    autosave=True,
+    dashboard=False,
+    verbose=True,
+    dynamic_temperature_enabled=True,
+    saved_state_path="delaware_c_corp_risk_analysis_agent.json",
+    user_name="risk_analyst_user",
+    retry_attempts=2,
+    context_length=200000,
+    long_term_memory=memory_risk,
+)
+
+# Initialize Sustainability Agent
+sustainability_agent = Agent(
+    agent_name="Delaware-C-Corp-Sustainability-Agent",
+    system_prompt="You are a sustainability analysis agent focused on ESG factors.",
+    agent_description="Analyzes sustainability practices for Delaware C Corps.",
+    llm=model,
+    max_loops=2,
+    autosave=True,
+    dashboard=False,
+    verbose=True,
+    dynamic_temperature_enabled=False,
+    saved_state_path="delaware_c_corp_sustainability_agent.json",
+    user_name="sustainability_specialist",
+    retry_attempts=3,
+    context_length=180000,
+    long_term_memory=memory_sustainability,
+)
+
+# Run the agents
+risk_analysis_agent.run("What are the top financial and operational risks for a Delaware C Corp in healthcare?")
+sustainability_agent.run("How can a Delaware C Corp in manufacturing improve its sustainability practices?")
+
+from reflection_tuner import ReflectionTuner
+
+# Initialize Reflection Tuners for each agent
+risk_reflection_tuner = ReflectionTuner(risk_analysis_agent, reflection_steps=2)
+sustainability_reflection_tuner = ReflectionTuner(sustainability_agent, reflection_steps=2)
+
+# Run the agents with Reflection Tuning
+risk_response = risk_reflection_tuner.reflect_and_tune("What are the top financial and operational risks for a Delaware C Corp in healthcare?")
+sustainability_response = sustainability_reflection_tuner.reflect_and_tune("How can a Delaware C Corp in manufacturing improve its sustainability practices?")
+
+print("Risk Analysis Agent Response:", risk_response)
+print("Sustainability Agent Response:", sustainability_response)
+
+# Initialize agents from agents_with_new.yaml
+# Import ReflectionTuner
+from reflection_tuner import ReflectionTuner
+
+# Initialize Reflection Tuner for all agents, including existing ones
+deduction_agent = Agent(
+    agent_name="Delaware-C-Corp-Tax-Deduction-Agent",
+    system_prompt="Provide expert advice on tax deductions for Delaware C Corps.",
+    agent_description="Analyzes tax deduction strategies.",
+    llm=model,
+    max_loops=1,
+    autosave=True,
+    dashboard=False,
+    verbose=True,
+    dynamic_temperature_enabled=True,
+    saved_state_path="delaware_c_corp_tax_deduction_agent.json",
+    user_name="swarms_corp",
+    retry_attempts=1,
+    context_length=250000,
+    long_term_memory=memory_risk,  # Reuse memory for testing purposes
+)
+
+optimization_agent = Agent(
+    agent_name="Delaware-C-Corp-Tax-Optimization-Agent",
+    system_prompt="Provide expert advice on tax optimization strategies for Delaware C Corps.",
+    agent_description="Analyzes tax optimization.",
+    llm=model,
+    max_loops=2,
+    autosave=True,
+    dashboard=False,
+    verbose=True,
+    dynamic_temperature_enabled=False,
+    saved_state_path="delaware_c_corp_tax_optimization_agent.json",
+    user_name="tax_optimization_user",
+    retry_attempts=3,
+    context_length=200000,
+    long_term_memory=memory_risk,
+)
+
+# Initialize Reflection Tuners
+deduction_reflection_tuner = ReflectionTuner(deduction_agent, reflection_steps=2)
+optimization_reflection_tuner = ReflectionTuner(optimization_agent, reflection_steps=2)
+
+# Run agents with Reflection Tuning
+deduction_response = deduction_reflection_tuner.reflect_and_tune("What are the most effective tax deduction strategies for a Delaware C Corp in tech?")
+optimization_response = optimization_reflection_tuner.reflect_and_tune("How can a Delaware C Corp in finance optimize its tax strategy?")
+
+print("Tax Deduction Agent Response:", deduction_response)
+print("Tax Optimization Agent Response:", optimization_response)

agent_with_all_agents_reflection_planner_collector.py

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+
+from swarms import Agent
+from swarm_models import OpenAIChat
+from swarms_memory import ChromaDB
+import os
+
+# Initialize memory for agents
+memory_risk = ChromaDB(metric="cosine", output_dir="risk_analysis_results")
+memory_sustainability = ChromaDB(metric="cosine", output_dir="sustainability_results")
+
+# Initialize model
+model = OpenAIChat(api_key=os.getenv("OPENAI_API_KEY"), model_name="gpt-4o-mini", temperature=0.1)
+
+# Initialize Risk Analysis Agent
+risk_analysis_agent = Agent(
+    agent_name="Delaware-C-Corp-Risk-Analysis-Agent",
+    system_prompt="You are a specialized risk analysis agent focused on assessing risks.",
+    agent_description="Performs risk analysis for Delaware C Corps.",
+    llm=model,
+    max_loops=3,
+    autosave=True,
+    dashboard=False,
+    verbose=True,
+    dynamic_temperature_enabled=True,
+    saved_state_path="delaware_c_corp_risk_analysis_agent.json",
+    user_name="risk_analyst_user",
+    retry_attempts=2,
+    context_length=200000,
+    long_term_memory=memory_risk,
+)
+
+# Initialize Sustainability Agent
+sustainability_agent = Agent(
+    agent_name="Delaware-C-Corp-Sustainability-Agent",
+    system_prompt="You are a sustainability analysis agent focused on ESG factors.",
+    agent_description="Analyzes sustainability practices for Delaware C Corps.",
+    llm=model,
+    max_loops=2,
+    autosave=True,
+    dashboard=False,
+    verbose=True,
+    dynamic_temperature_enabled=False,
+    saved_state_path="delaware_c_corp_sustainability_agent.json",
+    user_name="sustainability_specialist",
+    retry_attempts=3,
+    context_length=180000,
+    long_term_memory=memory_sustainability,
+)
+
+# Run the agents
+risk_analysis_agent.run("What are the top financial and operational risks for a Delaware C Corp in healthcare?")
+sustainability_agent.run("How can a Delaware C Corp in manufacturing improve its sustainability practices?")
+
+from reflection_tuner import ReflectionTuner
+
+# Initialize Reflection Tuners for each agent
+risk_reflection_tuner = ReflectionTuner(risk_analysis_agent, reflection_steps=2)
+sustainability_reflection_tuner = ReflectionTuner(sustainability_agent, reflection_steps=2)
+
+# Run the agents with Reflection Tuning
+risk_response = risk_reflection_tuner.reflect_and_tune("What are the top financial and operational risks for a Delaware C Corp in healthcare?")
+sustainability_response = sustainability_reflection_tuner.reflect_and_tune("How can a Delaware C Corp in manufacturing improve its sustainability practices?")
+
+print("Risk Analysis Agent Response:", risk_response)
+print("Sustainability Agent Response:", sustainability_response)
+
+# Initialize agents from agents_with_new.yaml
+# Import ReflectionTuner
+from reflection_tuner import ReflectionTuner
+
+# Initialize Reflection Tuner for all agents, including existing ones
+deduction_agent = Agent(
+    agent_name="Delaware-C-Corp-Tax-Deduction-Agent",
+    system_prompt="Provide expert advice on tax deductions for Delaware C Corps.",
+    agent_description="Analyzes tax deduction strategies.",
+    llm=model,
+    max_loops=1,
+    autosave=True,
+    dashboard=False,
+    verbose=True,
+    dynamic_temperature_enabled=True,
+    saved_state_path="delaware_c_corp_tax_deduction_agent.json",
+    user_name="swarms_corp",
+    retry_attempts=1,
+    context_length=250000,
+    long_term_memory=memory_risk,  # Reuse memory for testing purposes
+)
+
+optimization_agent = Agent(
+    agent_name="Delaware-C-Corp-Tax-Optimization-Agent",
+    system_prompt="Provide expert advice on tax optimization strategies for Delaware C Corps.",
+    agent_description="Analyzes tax optimization.",
+    llm=model,
+    max_loops=2,
+    autosave=True,
+    dashboard=False,
+    verbose=True,
+    dynamic_temperature_enabled=False,
+    saved_state_path="delaware_c_corp_tax_optimization_agent.json",
+    user_name="tax_optimization_user",
+    retry_attempts=3,
+    context_length=200000,
+    long_term_memory=memory_risk,
+)
+
+# Initialize Reflection Tuners
+deduction_reflection_tuner = ReflectionTuner(deduction_agent, reflection_steps=2)
+optimization_reflection_tuner = ReflectionTuner(optimization_agent, reflection_steps=2)
+
+# Run agents with Reflection Tuning
+deduction_response = deduction_reflection_tuner.reflect_and_tune("What are the most effective tax deduction strategies for a Delaware C Corp in tech?")
+optimization_response = optimization_reflection_tuner.reflect_and_tune("How can a Delaware C Corp in finance optimize its tax strategy?")
+
+print("Tax Deduction Agent Response:", deduction_response)
+print("Tax Optimization Agent Response:", optimization_response)
+
+from reflection_tuner import ReflectionTuner
+import requests
+
+def duckduckgo_search(query):
+    # Simple DuckDuckGo search function for Data-Collector agent
+    url = f"https://api.duckduckgo.com/?q={query}&format=json&pretty=1"
+    response = requests.get(url)
+    if response.status_code == 200:
+        return response.json().get("AbstractText", "No data found")
+    return "Failed to retrieve data"
+
+# Initialize Planner and Data-Collector agents with DuckDuckGo search capability
+planner_agent = Agent(
+    agent_name="Delaware-C-Corp-Planner-Agent",
+    system_prompt="Develop a quarterly strategic roadmap for a Delaware C Corp.",
+    agent_description="Creates detailed plans and schedules.",
+    llm=model,
+    max_loops=2,
+    autosave=True,
+    dashboard=False,
+    verbose=True,
+    dynamic_temperature_enabled=True,
+    saved_state_path="delaware_c_corp_planner_agent.json",
+    user_name="planner_user",
+    retry_attempts=2,
+    context_length=150000,
+    long_term_memory=memory_sustainability,  # Reuse memory for demonstration purposes
+)
+
+data_collector_agent = Agent(
+    agent_name="Delaware-C-Corp-Data-Collector-Agent",
+    system_prompt="Collect and synthesize information from DuckDuckGo search.",
+    agent_description="Gathers data from open-source search engines.",
+    llm=model,
+    max_loops=3,
+    autosave=True,
+    dashboard=False,
+    verbose=True,
+    dynamic_temperature_enabled=True,
+    saved_state_path="delaware_c_corp_data_collector_agent.json",
+    user_name="data_collector_user",
+    retry_attempts=3,
+    context_length=200000,
+    long_term_memory=memory_risk,  # Reuse memory for demonstration
+)
+
+# Initialize Reflection Tuners
+planner_reflection_tuner = ReflectionTuner(planner_agent, reflection_steps=2)
+data_collector_reflection_tuner = ReflectionTuner(data_collector_agent, reflection_steps=2)
+
+# Run Planner agent with Reflection Tuning
+planner_response = planner_reflection_tuner.reflect_and_tune("Create a quarterly strategic roadmap for a Delaware C Corp in biotech.")
+print("Planner Agent Response:", planner_response)
+
+# Run Data Collector agent with Reflection Tuning, using DuckDuckGo search
+data_collector_task = "Find recent trends in tax strategies for corporations in the US."
+search_result = duckduckgo_search(data_collector_task)
+data_collector_response = data_collector_reflection_tuner.reflect_and_tune(f"{search_result}")
+print("Data Collector Agent Response:", data_collector_response)